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Specific Course Information
To fulfill the requirements of the General Education Elective or General Electives, students may choose from the three approved lists found in section 71.110 of the current Undergraduate Calendar. These include Social Sciences, Humanities and Other Complementary Studies.
Students in the Extended Credit Program (ECP) or the Mature Entry Program (MEP) (see §14.2.3) or any other students who have been assigned credits in Humanities and Social Sciences must select those credits from Social Sciences and Humanities only. Those credits cannot be chosen from the list of Other Complementary Studies.
If students wish to take a course not listed, they must receive written permission from the Student Academic Services (SAS) Office of the Gina Cody School of Engineering and Computer Science prior to taking the course.
This is a one-credit project course set up to meet the special needs of certain students lacking one credit or less for graduation. It is a technical elective. Registration into this course requires the written permission from the Department of Computer Science and Software Engineering.
This is a one-credit project course set up to meet the special needs of certain SOEN students lacking one credit or less for graduation. It is a technical elective. Registration into this course requires the written permission from the Department of Computer Science and Software Engineering.
COMP 498 Neuroimage Computing (3 credits)
Prerequisites: ENGR 371 or COMP233. This course covers concepts, theories and practical knowledge in brain image processing and analysis. A practical introduction of medical imaging principles and image reconstruction will be provided. Topics to be covered include brain atlasing, computational anatomy, radiomics, tractography, image segmentation/classification, deep learning in neuroimaging, image-guided neurosurgery, and computer-assisted diagnosis. State-of-the-art software for neuroimage processing and analysis, as well as popular open source databases will also be covered through assignments and a project. Lectures: 3 hours per week.
COMP 499 Topics in Computer Science with Lab: Deep Learning (4 credits)
Prerequisite: COMP 432 or permission of instructor. This course introduces conceptual and practical aspects of deep learning. Topics covered will include commonly used deep learning model architectures, loss functions, regularization, optimization methods, and software tools. We will study applications in supervised, unsupervised learning, and reinforcement learning. Finally, the course will also aim to familiarize the students with theoretical results and open questions in the approximation and generalization of deep learning models as well as empirical results about the emergent properties of deep networks. A final project will be required. Lectures: three hours per week. Laboratory: two hours per week.
2021 Open House
- 2021-2022 Admission requirements for the Bachelor of Computer Science (BCompSc)
- 2021-2022 Admission requirements for the Bachelor of Engineering, Software Engineering (BEng)
- Differences between the Computer Science and Software Engineering programs
- NEW: Bachelor of Computer Science in Health and Life Sciences (Available Fall 2021)
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Undergraduate Program Assistants
EV 3.144, Ext: 3053
Undergraduate Program Directors
Dr. Nematollaah Shiri
EV 3.411, Ext: 3018
Dr. Nikolaos Tsantalis S-EV 3187, Ext. 3020
Dr. Rajajopalan Jayakumar
S-EV 3.151, Ext. 3011